Widera, PawelWelsing, Paco M.J.Danso, Samuel O.Peelen, SjaakKloppenburg, MargreetLoef, MariekeMarijnissen, Anne C.A.van Helvoort, Eefje M.Blanco García, Francisco JMagalhães, JoanaBerenbaum, FrancisHaugen, Ida KristinBay-Jensen, Anne CMobasheri, AliLadel, ChristophLoughlin, JohnLafeber, FlorisLalande, AgnesLarkin, JonathanWeinans, HarrieBacardit, Jaume2023-09-042023-09-042023-12Widera P, Welsing PMJ, Danso SO, Peelen S, Kloppenburg M, Loef M, Marijnissen AC, van Helvoort EM, Blanco FJ, Magalhães J, Berenbaum F, Haugen IK, Bay-Jensen AC, Mobasheri A, Ladel C, Loughlin J, Lafeber FPJG, Lalande A, Larkin J, Weinans H, Bacardit J. Development and validation of a machine learning-supported strategy of patient selection for osteoarthritis clinical trials: the IMI-APPROACH study. Osteoarthr Cartil Open. 2023 Aug 18;5(4):100406.2665-9131http://hdl.handle.net/2183/33427[Abstract] Objectives. To efficiently assess the disease-modifying potential of new osteoarthritis treatments, clinical trials need progression-enriched patient populations. To assess whether the application of machine learning results in patient selection enrichment, we developed a machine learning recruitment strategy targeting progressive patients and validated it in the IMI-APPROACH knee osteoarthritis prospective study. Design. We designed a two-stage recruitment process supported by machine learning models trained to rank candidates by the likelihood of progression. First stage models used data from pre-existing cohorts to select patients for a screening visit. The second stage model used screening data to inform the final inclusion. The effectiveness of this process was evaluated using the actual 24-month progression. Results. From 3500 candidate patients, 433 with knee osteoarthritis were screened, 297 were enrolled, and 247 completed the 2-year follow-up visit. We observed progression related to pain (P, 30%), structure (S, 13%), and combined pain and structure (P ​+ ​S, 5%), and a proportion of non-progressors (N, 52%) ∼15% lower vs an unenriched population. Our model predicted these outcomes with AUC of 0.86 [95% CI, 0.81–0.90] for pain-related progression and AUC of 0.61 [95% CI, 0.52–0.70] for structure-related progression. Progressors were ranked higher than non-progressors for P ​+ ​S (median rank 65 vs 143, AUC = 0.75), P (median rank 77 vs 143, AUC = 0.71), and S patients (median rank 107 vs 143, AUC = 0.57). Conclusions. The machine learning-supported recruitment resulted in enriched selection of progressive patients. Further research is needed to improve structural progression prediction and assess this strategy in an interventional trial.engCreative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC-BY-NC-ND 4.0)http://creativecommons.org/licenses/by-nc-nd/4.0/OsteoarthritisDisease progression predictionMachine learningPatient selection for clinical trialsInclusionjournal articleopen access